agentleFS
OneWave-AIOneWave-AI/claude-skills/agent-to-agent/SKILL.md

agent-to-agent

Skill378 starsChanged 52 days agoMITView on GitHub

Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Treat .a2a-context.json as the single source of truth.

    line 35
  2. Very specific:

    Never write secrets to .a2a-context.json.

    line 38

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Agent-to-Agent (A2A) Communication Protocol

Act as the A2A Coordinator: a protocol layer that lets multiple Claude Code agents communicate, collaborate, and delegate work through structured message passing, shared context, and formal handoffs. Orchestrate every interaction through the shared context file .a2a-context.json and the Agent tool.

Contents

  • references/protocol.md — message format, message types, lifecycle, shared context schema, atomic read-modify-write, context size management.
  • references/registry.md — agent registration, capability discovery, built-in agent templates.
  • references/patterns.md — request/response, pipeline, fan-out/fan-in, conversation, supervisor.
  • references/handoff.md — structured handoff, acceptance, rejection, chain tracking.
  • references/error-handling.md — timeouts, rejections, deadlock detection, degradation, escalation matrix.
  • references/workflows.md — worked examples (research+writer, code+review, sales+technical).
  • references/operations.md — coordination commands, best practices, monitoring, security, init detail.

Workflow

  1. Understand the goal. Determine what the user wants to accomplish with multiple agents.
  2. Design the team. Decide which agents are needed; draw from the templates in references/registry.md or write custom specs.
  3. Choose the pattern. Select pipeline, fan-out/fan-in, conversation, or supervisor from references/patterns.md. Prefer pipeline when order matters, fan-out when subtasks are independent.
  4. Initialize. Locate the project root. Read .a2a-context.json if it exists and report current state; otherwise create it from the template in references/operations.md. Register every agent into the agents section per references/registry.md.
  5. Execute. Dispatch agents via the Agent tool following the chosen pattern. Structure each agent prompt with identity, context, task, output location, protocol, and constraints (see references/operations.md). For parallelism, issue multiple Agent tool calls in a single response.
  6. Coordinate handoffs. When an agent transfers a task, require a full handoff payload and an ACK, and append to the task chain. Follow references/handoff.md.
  7. Monitor and recover. Read .a2a-context.json to track progress. On timeout, rejection, deadlock, or failure, apply the procedures and escalation matrix in references/error-handling.md. Cap retries at 3 before escalating to the user.
  8. Deliver. Merge all agent findings into the conclusions section and present the final output.

Core Rules

  • Treat .a2a-context.json as the single source of truth. Read it before acting; write the complete file back after modifying. Follow the atomic read-modify-write procedure in references/protocol.md.
  • Conform every inter-agent message to the schema in references/protocol.md.
  • Confine each agent to writing its own section plus shared conclusions. Restrict task assignment changes to the Coordinator.
  • Never write secrets to .a2a-context.json; pass sensitive data in-memory through Agent prompts and add the file to .gitignore.
  • Require explicit ERROR messages for all failures; never fail silently. Run context summarization when the file exceeds 50KB.

Minimal Example: 2-Agent Pipeline

User: "Research the top 5 AI frameworks and write a comparison article."

Coordinator:
1. Create .a2a-context.json
2. Register: researcher, writer
3. Dispatch researcher: "Search for top 5 AI frameworks, compare features, performance, ecosystem"
4. Read researcher's findings from shared context
5. Dispatch writer: "Using the research findings, write a 1200-word comparison article"
6. Read writer's draft from shared context
7. Present the final article to the user

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.